Databricks Certified Machine Learning Professional: Databricks-Machine-Learning-Professional Exam
"Databricks Certified Machine Learning Professional", also known as Databricks-Machine-Learning-Professional exam, is a Databricks Certification. With the complete collection of questions and answers, PrepAwayPDF has assembled to take you through 193 Q&As to your Databricks-Machine-Learning-Professional Exam preparation. In the Databricks-Machine-Learning-Professional exam resources, you will cover every field and category in ML Data Scientist Certification helping to ready you for your successful Databricks Certification.
- Exam Code: Databricks-Machine-Learning-Professional
- Exam Name: Databricks Certified Machine Learning Professional
- Certification Provider: Databricks
- Corresponding Certification: ML Data Scientist
- Updated: Oct 11, 2026
- No. of Questions: 193 Questions & Answers with Testing Engine
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Affordable, latest, and comprehensive — why settle for fewer than three? PrepAwayPDF's Databricks-Machine-Learning-Professional bank delivers 193 expert-verified practice questions covering the Databricks Certified Machine Learning Professional objectives, updated constantly through 2026.
Databricks Databricks-Machine-Learning-Professional Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Machine Learning Professional Exam |
| Exam Number: | Databricks-Machine-Learning-Professional |
| Related Certifications: | Databricks Certified Machine Learning Associate |
| Exam Price: | $200 USD |
| Exam Format: | Multiple choice, Multi-select |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 59 |
| Exam Duration: | 120 minutes |
| Passing Score: | Not publicly disclosed (approximately 70%) |
| Recommended Training: | Machine Learning at Scale Advanced Machine Learning Operations |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored or in-person test center |
| Pre Condition: | No mandatory prerequisites; recommended: 6+ months hands-on experience with Databricks ML, SparkML, MLflow, and Python |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/machine-learning-professional |
Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Model Development | 44% | - Feature Store and automated feature pipelines - Scalable ML pipelines with SparkML - Advanced MLflow usage - Distributed training and hyperparameter tuning |
| Model Deployment | 12% | - Deployment strategies - Model rollout and version management - Custom model serving |
| ML Ops | 44% | - Automated retraining workflows - Testing and validation strategies - Model monitoring and drift detection with Lakehouse Monitoring - Environment management with Databricks Asset Bundles |
Databricks-Machine-Learning-Professional Exam Essentials and Our Service System
Currently, the Databricks-Machine-Learning-Professional exam's passing score is Not publicly disclosed (approximately 70%) and the registration fee is $200 USD. Both are Databricks's figures and subject to revision — verify them on the official site before you schedule.
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The Databricks Certified Machine Learning Professional blueprint covers these core domains:
- Model Deployment (12%)
- Model Development (44%)
- ML Ops (44%)
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Databricks states the following prerequisites for the Databricks Certified Machine Learning Professional: No mandatory prerequisites; recommended: 6+ months hands-on experience with Databricks ML, SparkML, MLflow, and Python.
Confirm the details on the official certification page before registering.
According to the latest exam information, the Databricks-Machine-Learning-Professional exam includes 59 questions and allows 120 minutes minutes. The online engine reproduces those conditions so practice matches the formal test's atmosphere.
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Databricks Certified Machine Learning Professional Sample Questions:
A machine learning engineering team has decided that they need to have predictions be made available for querying in continuous, equal-sized increments. A computation can be included in one of the increments when all of its feature values are in the inference Spark DataFrame. Which of the following tools can be used to provide this type of continuous inference?
- A. Delta Lake
- B. Structured Streaming
- C. Spark UDFs
- D. MLflow
Correct Answer: B 🗳️
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Which of the following Databricks-managed MLflow capabilities is a centralized model store?
- A. Model Serving
- B. Feature Store
- C. Model Registry
- D. Experiments
- E. Models
Correct Answer: C 🗳️
A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks. Which location in Databricks will show these data visualizations?
- A. The MLflow Model Registry Model paqe
- B. The Artifacts section of the MLflow Experiment page
- C. Logged data visualizations cannot be viewed in Databricks
- D. The Figures section of the MLflow Run page
- E. The Artifacts section of the MLflow Run page
Correct Answer: D 🗳️
A Data Scientist is tasked with developing models to forecast product demand. The company offers 5000 different product types, and the Data Scientist must generate weekly forecasts for each type. They have access to two years of historical purchase data and are given ample project budget.
For their next project, they want to build 5000 separate Random Forest models, one for each product type. They aim to train all the models as quickly as possible with minimal setup.
Which approach meets these requirements?
- A. Use the DeepSpeed library to distribute the data by product across different nodes to enable the parallel training of multiple models.
- B. Leverage the pandas function API (Grouped map) to group the data by product type and apply a custom model training function to each group.
- C. Use the RandomForest method from MLlib. This will leverage Spark's parallel processing capability to train 5000 different models.
- D. Create a Databricks Workflow with 5000 tasks. Each task is configured to accept a product ID as a parameter which will then train a model based on the specified product ID.
Correct Answer: B 🗳️
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A Machine Learning Engineer needs to develop a custom anomaly detection model that monitors the internal IT infrastructure of their company. The model takes in compute metrics, logs, and user data and generates a binary prediction. The engineer plans to deploy it as a Databricks Model Serving endpoint. In production there will only be one client calling the endpoint once every
15 seconds. Leadership sees the model as an important part of their operational improvement strategy so maintaining consistent, stable, low latency inference is a requirement while minimizing infrastructure costs. The engineer plans to deploy the endpoint via the MLflow Deployment SDK.
Which endpoint config for the MLflow Deployment SDK should the engineer select?
- A.
- B.
- C.
- D.
Correct Answer: C 🗳️
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